Enhancing the Efficiency of Detecting Intrusions using Improved PSOGMM

Network security is one of the most significant problems in computer network management and intrusion. In recent years, the intrusion has occurred as a major area of ​​security for the network. Each section of the attacks is considered to be a particular problem and IDS are doing well when specialized algorithms are handled. Several surveys show that penetration in the network has been steadily increased and has led to private privacy theft. It is an important platform for recent attacks. A network intrusion is illegal activities in the computer network. It is, therefore, necessary to improve an operative intrusion system. In this paper, we use improved particle swarm optimization Gaussian mixture model (IPSOGMM) to detect infiltrative inspection. This paper shows compatibility between an integrated system using an IGKM algorithm and an interchange control system used by the IPSOGMM algorithm in the KDD-99 dataset. Finding that the test was discovered uses IPSOGMM algorithm is additionally correct when compared to IGKM algorithm.

[1]  R.K. Cunningham,et al.  Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation , 2000, Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00.

[2]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[3]  C. Apte,et al.  Data mining with decision trees and decision rules , 1997, Future Gener. Comput. Syst..

[4]  Wei-Yang Lin,et al.  Intrusion detection by machine learning: A review , 2009, Expert Syst. Appl..

[5]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[6]  Robert P. W. Duin,et al.  Precision-recall operating characteristic (P-ROC) curves in imprecise environments , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[7]  Salvatore J. Stolfo,et al.  A framework for constructing features and models for intrusion detection systems , 2000, TSEC.

[8]  WangWei,et al.  Processing of massive audit data streams for real-time anomaly intrusion detection , 2008 .

[9]  Gabriel Maciá-Fernández,et al.  Anomaly-based network intrusion detection: Techniques, systems and challenges , 2009, Comput. Secur..

[10]  Hong Shen,et al.  Application of online-training SVMs for real-time intrusion detection with different considerations , 2005, Comput. Commun..

[11]  M. N. Masrek,et al.  Comparison of Machine Learning algorithms performance in detecting network intrusion , 2010, 2010 International Conference on Networking and Information Technology.

[12]  John E. Gaffney,et al.  Evaluation of intrusion detectors: a decision theory approach , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.